卡尔曼滤波器
结构健康监测
加速度计
悬挂(拓扑)
风速计
情态动词
工程类
风速
控制理论(社会学)
集合卡尔曼滤波器
跨度(工程)
空气动力学
振动
风洞
扩展卡尔曼滤波器
风力工程
高斯过程
结构工程
高斯分布
计算机科学
气象学
声学
航空航天工程
数学
地理
物理
人工智能
化学
高分子化学
纯数学
操作系统
控制(管理)
量子力学
同伦
作者
Øyvind Wiig Petersen,Ole Øiseth,Eliz-Mari Lourens
标识
DOI:10.1016/j.ymssp.2021.108742
摘要
Wind loading is an essential aspect in the design and assessment of long-span bridges, but it is often not well-known and cannot be measured directly. Most structural health monitoring systems can easily measure structural responses at discrete locations using accelerometers. This data can be combined with reduced-order modal models in Kalman filter-based algorithms for an inverse estimation of wind loads and system states. As a further development, this work investigates the incorporation of Gaussian process latent force models (GP-LFMs), which can characterize the evolution of the wind loading. The Hardanger Bridge, a 1310 m long suspension bridge instrumented with a monitoring system for wind and vibrations, is used as a case study. It is shown how the LFMs can be enriched with physical information about the stochastic wind loads using monitoring anemometer data and aerodynamic coefficients from wind tunnel tests. It is found that the estimates of the modal wind loads and modal states obtained from a Kalman filter and Rauch–Tung–Striebel smoother are stable for acceleration output only, thus avoiding the accumulation of errors. The proposed approach demonstrates how physical or environmental data can be injected as valuable information for global monitoring strategies and virtual sensing in bridges.
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